code: def _gaussian_kernel_convolve(chol, norm, target, weights, mean):
      diff = target - mean[:, None]
      alpha = linalg.cho_solve(chol, diff)
      arg = 0.5 * jnp.sum(diff * alpha, axis=0)
      return norm * jnp.sum(jnp.exp(-arg) * weights)
  
imports: from dataclasses import dataclass
  from functools import partial
  from typing import Any
  import numpy
  import scipy.stats
  import jax.numpy
  from jax import jit, lax, random, vmap
  from jax._src.numpy.util import check_arraylike, promote_dtypes_inexact, _wraps
  from jax._src.tree_util import register_pytree_node_class
  from jax.scipy import linalg, special
label_desc: Use image node_id 1 for calling a global function with example usage: _gaussian_kernel_convolve(chol, norm, target, weights, mean) and returns: unknown